Overview

Dataset statistics

Number of variables37
Number of observations132
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.3 KiB
Average record size in memory297.0 B

Variable types

Categorical18
Numeric19

Alerts

Farm.Name has a high cardinality: 57 distinct values High cardinality
Producer has a high cardinality: 62 distinct values High cardinality
Grading.Date has a high cardinality: 60 distinct values High cardinality
Expiration has a high cardinality: 60 distinct values High cardinality
Altitude is highly correlated with altitude_low_meters and 2 other fieldsHigh correlation
Aroma is highly correlated with Flavor and 6 other fieldsHigh correlation
Flavor is highly correlated with Aroma and 6 other fieldsHigh correlation
Aftertaste is highly correlated with Aroma and 6 other fieldsHigh correlation
Acidity is highly correlated with Aroma and 6 other fieldsHigh correlation
Body is highly correlated with Aroma and 6 other fieldsHigh correlation
Balance is highly correlated with Aroma and 6 other fieldsHigh correlation
Uniformity is highly correlated with Clean.CupHigh correlation
Clean.Cup is highly correlated with Uniformity and 1 other fieldsHigh correlation
Sweetness is highly correlated with Clean.CupHigh correlation
Cupper.Points is highly correlated with Aroma and 6 other fieldsHigh correlation
Total.Cup.Points is highly correlated with Aroma and 6 other fieldsHigh correlation
altitude_low_meters is highly correlated with Altitude and 2 other fieldsHigh correlation
altitude_high_meters is highly correlated with Altitude and 2 other fieldsHigh correlation
altitude_mean_meters is highly correlated with Altitude and 2 other fieldsHigh correlation
Altitude is highly correlated with altitude_low_meters and 2 other fieldsHigh correlation
Aroma is highly correlated with Flavor and 5 other fieldsHigh correlation
Flavor is highly correlated with Aroma and 5 other fieldsHigh correlation
Aftertaste is highly correlated with Aroma and 5 other fieldsHigh correlation
Acidity is highly correlated with Aroma and 5 other fieldsHigh correlation
Body is highly correlated with Aroma and 5 other fieldsHigh correlation
Balance is highly correlated with Aroma and 5 other fieldsHigh correlation
Uniformity is highly correlated with Clean.Cup and 2 other fieldsHigh correlation
Clean.Cup is highly correlated with Uniformity and 2 other fieldsHigh correlation
Sweetness is highly correlated with Uniformity and 2 other fieldsHigh correlation
Cupper.Points is highly correlated with Total.Cup.PointsHigh correlation
Total.Cup.Points is highly correlated with Aroma and 9 other fieldsHigh correlation
altitude_low_meters is highly correlated with Altitude and 2 other fieldsHigh correlation
altitude_high_meters is highly correlated with Altitude and 2 other fieldsHigh correlation
altitude_mean_meters is highly correlated with Altitude and 2 other fieldsHigh correlation
Altitude is highly correlated with altitude_low_meters and 2 other fieldsHigh correlation
Aroma is highly correlated with Flavor and 1 other fieldsHigh correlation
Flavor is highly correlated with Aroma and 6 other fieldsHigh correlation
Aftertaste is highly correlated with Flavor and 4 other fieldsHigh correlation
Acidity is highly correlated with Flavor and 2 other fieldsHigh correlation
Body is highly correlated with Flavor and 3 other fieldsHigh correlation
Balance is highly correlated with Aroma and 6 other fieldsHigh correlation
Uniformity is highly correlated with Clean.CupHigh correlation
Clean.Cup is highly correlated with Uniformity and 1 other fieldsHigh correlation
Sweetness is highly correlated with Clean.CupHigh correlation
Cupper.Points is highly correlated with Flavor and 3 other fieldsHigh correlation
Total.Cup.Points is highly correlated with Flavor and 5 other fieldsHigh correlation
altitude_low_meters is highly correlated with Altitude and 2 other fieldsHigh correlation
altitude_high_meters is highly correlated with Altitude and 2 other fieldsHigh correlation
altitude_mean_meters is highly correlated with Altitude and 2 other fieldsHigh correlation
Owner.1 is highly correlated with Species and 12 other fieldsHigh correlation
unit_of_measurement is highly correlated with Farm.Name and 2 other fieldsHigh correlation
Harvest.Year is highly correlated with Farm.Name and 3 other fieldsHigh correlation
Species is highly correlated with Owner.1 and 9 other fieldsHigh correlation
Owner is highly correlated with Owner.1 and 12 other fieldsHigh correlation
Farm.Name is highly correlated with Owner.1 and 15 other fieldsHigh correlation
Grading.Date is highly correlated with Owner.1 and 12 other fieldsHigh correlation
Variety is highly correlated with Owner.1 and 10 other fieldsHigh correlation
Processing.Method is highly correlated with Owner.1 and 9 other fieldsHigh correlation
Country.of.Origin is highly correlated with Owner.1 and 11 other fieldsHigh correlation
In.Country.Partner is highly correlated with Owner.1 and 9 other fieldsHigh correlation
Color is highly correlated with Farm.Name and 2 other fieldsHigh correlation
Expiration is highly correlated with Owner.1 and 12 other fieldsHigh correlation
Region is highly correlated with Owner.1 and 11 other fieldsHigh correlation
Company is highly correlated with Owner.1 and 12 other fieldsHigh correlation
Producer is highly correlated with Owner.1 and 14 other fieldsHigh correlation
Bag.Weight is highly correlated with Owner.1 and 6 other fieldsHigh correlation
Species is highly correlated with Owner and 10 other fieldsHigh correlation
Owner is highly correlated with Species and 22 other fieldsHigh correlation
Country.of.Origin is highly correlated with Species and 21 other fieldsHigh correlation
Farm.Name is highly correlated with Species and 24 other fieldsHigh correlation
Company is highly correlated with Species and 27 other fieldsHigh correlation
Altitude is highly correlated with Producer and 3 other fieldsHigh correlation
Region is highly correlated with Species and 28 other fieldsHigh correlation
Producer is highly correlated with Species and 31 other fieldsHigh correlation
Number.of.Bags is highly correlated with Owner and 22 other fieldsHigh correlation
Bag.Weight is highly correlated with Owner and 16 other fieldsHigh correlation
In.Country.Partner is highly correlated with Owner and 16 other fieldsHigh correlation
Harvest.Year is highly correlated with Owner and 11 other fieldsHigh correlation
Grading.Date is highly correlated with Species and 26 other fieldsHigh correlation
Owner.1 is highly correlated with Species and 22 other fieldsHigh correlation
Variety is highly correlated with Species and 19 other fieldsHigh correlation
Processing.Method is highly correlated with Owner and 15 other fieldsHigh correlation
Aroma is highly correlated with Owner and 18 other fieldsHigh correlation
Flavor is highly correlated with Farm.Name and 17 other fieldsHigh correlation
Aftertaste is highly correlated with Owner and 19 other fieldsHigh correlation
Acidity is highly correlated with Farm.Name and 17 other fieldsHigh correlation
Body is highly correlated with Owner and 21 other fieldsHigh correlation
Balance is highly correlated with Owner and 20 other fieldsHigh correlation
Uniformity is highly correlated with Flavor and 8 other fieldsHigh correlation
Clean.Cup is highly correlated with Number.of.Bags and 9 other fieldsHigh correlation
Sweetness is highly correlated with Species and 17 other fieldsHigh correlation
Cupper.Points is highly correlated with Owner and 20 other fieldsHigh correlation
Total.Cup.Points is highly correlated with Company and 15 other fieldsHigh correlation
Moisture is highly correlated with Owner and 9 other fieldsHigh correlation
Category.One.Defects is highly correlated with Grading.Date and 1 other fieldsHigh correlation
Quakers is highly correlated with Region and 4 other fieldsHigh correlation
Color is highly correlated with Owner and 9 other fieldsHigh correlation
Category.Two.Defects is highly correlated with Farm.Name and 8 other fieldsHigh correlation
Expiration is highly correlated with Species and 26 other fieldsHigh correlation
unit_of_measurement is highly correlated with Farm.Name and 6 other fieldsHigh correlation
altitude_low_meters is highly correlated with Altitude and 4 other fieldsHigh correlation
altitude_high_meters is highly correlated with Altitude and 4 other fieldsHigh correlation
altitude_mean_meters is highly correlated with Altitude and 4 other fieldsHigh correlation
Moisture has 20 (15.2%) zeros Zeros
Category.One.Defects has 124 (93.9%) zeros Zeros
Quakers has 86 (65.2%) zeros Zeros
Category.Two.Defects has 26 (19.7%) zeros Zeros

Reproduction

Analysis started2022-01-07 10:59:26.044723
Analysis finished2022-01-07 11:00:25.949817
Duration59.91 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Species
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Arabica
130 
Robusta
 
2

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArabica
2nd rowArabica
3rd rowArabica
4th rowArabica
5th rowArabica

Common Values

ValueCountFrequency (%)
Arabica130
98.5%
Robusta2
 
1.5%

Length

2022-01-07T12:00:26.086270image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-07T12:00:26.186383image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
arabica130
98.5%
robusta2
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Owner
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
juan luis alvarado romero
45 
ipanema coffees
23 
lin, che-hao krude 林哲豪
13 
bismarck castro
11 
consejo salvadoreño del café
Other values (19)
33 

Length

Max length41
Median length22
Mean length20.20454545
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)6.1%

Sample

1st rowlin, che-hao krude 林哲豪
2nd rowlin, che-hao krude 林哲豪
3rd rowconsejo salvadoreño del café
4th rowrodrigo soto
5th rowjuan luis alvarado romero

Common Values

ValueCountFrequency (%)
juan luis alvarado romero45
34.1%
ipanema coffees23
17.4%
lin, che-hao krude 林哲豪13
 
9.8%
bismarck castro11
 
8.3%
consejo salvadoreño del café7
 
5.3%
ceca, s.a.4
 
3.0%
exportadora atlantic, s.a.3
 
2.3%
janny marlith torres2
 
1.5%
gabriel barbara2
 
1.5%
elsy reyes2
 
1.5%
Other values (14)20
15.2%

Length

2022-01-07T12:00:26.291212image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
juan45
 
10.9%
alvarado45
 
10.9%
romero45
 
10.9%
luis45
 
10.9%
ipanema23
 
5.6%
coffees23
 
5.6%
lin13
 
3.2%
che-hao13
 
3.2%
krude13
 
3.2%
林哲豪13
 
3.2%
Other values (54)133
32.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Country.of.Origin
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Guatemala
45 
Brazil
26 
Honduras
17 
Taiwan
13 
Costa Rica
Other values (8)
23 

Length

Max length28
Median length9
Mean length8.090909091
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)2.3%

Sample

1st rowTaiwan
2nd rowTaiwan
3rd rowEl Salvador
4th rowCosta Rica
5th rowGuatemala

Common Values

ValueCountFrequency (%)
Guatemala45
34.1%
Brazil26
19.7%
Honduras17
 
12.9%
Taiwan13
 
9.8%
Costa Rica8
 
6.1%
El Salvador7
 
5.3%
Nicaragua5
 
3.8%
Uganda4
 
3.0%
Indonesia2
 
1.5%
India2
 
1.5%
Other values (3)3
 
2.3%

Length

2022-01-07T12:00:26.438939image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
guatemala45
30.0%
brazil26
17.3%
honduras17
 
11.3%
taiwan13
 
8.7%
costa8
 
5.3%
rica8
 
5.3%
el7
 
4.7%
salvador7
 
4.7%
nicaragua5
 
3.3%
uganda4
 
2.7%
Other values (8)10
 
6.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Farm.Name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct57
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
fazenda capoeirnha
13 
capoeirinha
10 
la esmeralda
 
7
la esperanza
 
6
las merceditas
 
5
Other values (52)
91 

Length

Max length33
Median length12
Mean length14.06060606
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)25.0%

Sample

1st rowtsoustructive garden 鄒築園
2nd rowshi fang yuan 十方源
3rd rowmonterrey
4th rowrio jorco
5th rowsan diego buena vista

Common Values

ValueCountFrequency (%)
fazenda capoeirnha13
 
9.8%
capoeirinha10
 
7.6%
la esmeralda7
 
5.3%
la esperanza6
 
4.5%
las merceditas5
 
3.8%
los hicaques5
 
3.8%
piamonte5
 
3.8%
las cuchillas5
 
3.8%
chapultepec4
 
3.0%
las delicias4
 
3.0%
Other values (47)68
51.5%

Length

2022-01-07T12:00:26.587706image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
las20
 
7.1%
la17
 
6.0%
fazenda13
 
4.6%
capoeirnha13
 
4.6%
capoeirinha10
 
3.6%
finca9
 
3.2%
coffee7
 
2.5%
esmeralda7
 
2.5%
esperanza6
 
2.1%
los6
 
2.1%
Other values (99)173
61.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Company
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct32
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
unex guatemala, s.a.
32 
ipanema coffees
23 
taiwan coffee laboratory
13 
cigrah s.a de c.v
11 
consejo salvadoreño del café
 
4
Other values (27)
49 

Length

Max length44
Median length20
Mean length19.24242424
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)9.1%

Sample

1st rowtaiwan coffee laboratory
2nd rowtaiwan coffee laboratory
3rd rowcafetalera del pacifico
4th rowpanamerican coffee trading
5th rowwaelti schoenfeld exportadores de cafe, s.a.

Common Values

ValueCountFrequency (%)
unex guatemala, s.a.32
24.2%
ipanema coffees23
17.4%
taiwan coffee laboratory13
 
9.8%
cigrah s.a de c.v11
 
8.3%
consejo salvadoreño del café4
 
3.0%
ceca,s.a.4
 
3.0%
mercon guatemala s.a.3
 
2.3%
exportadora atlantic s.a3
 
2.3%
volcafe ltda.3
 
2.3%
siembras vision, s.a.3
 
2.3%
Other values (22)33
25.0%

Length

2022-01-07T12:00:26.749711image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s.a59
15.9%
guatemala35
 
9.4%
unex32
 
8.6%
ipanema23
 
6.2%
coffees23
 
6.2%
coffee19
 
5.1%
de15
 
4.0%
taiwan13
 
3.5%
laboratory13
 
3.5%
cigrah13
 
3.5%
Other values (63)127
34.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Altitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct50
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3026.639697
Minimum16.8
Maximum190164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:26.922079image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum16.8
5-th percentile650
Q1941.5
median1400
Q31700
95-th percentile4000
Maximum190164
Range190147.2
Interquartile range (IQR)758.5

Descriptive statistics

Standard deviation16440.12152
Coefficient of variation (CV)5.431806614
Kurtosis131.0964156
Mean3026.639697
Median Absolute Deviation (MAD)400
Skewness11.43084947
Sum399516.44
Variance270277595.7
MonotonicityNot monotonic
2022-01-07T12:00:27.115487image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170011
 
8.3%
89011
 
8.3%
400010
 
7.6%
15009
 
6.8%
14007
 
5.3%
12505
 
3.8%
9345
 
3.8%
12004
 
3.0%
14504
 
3.0%
18504
 
3.0%
Other values (40)62
47.0%
ValueCountFrequency (%)
16.81
 
0.8%
3502
 
1.5%
5183
 
2.3%
6502
 
1.5%
6801
 
0.8%
7501
 
0.8%
8001
 
0.8%
8721
 
0.8%
89011
8.3%
8942
 
1.5%
ValueCountFrequency (%)
1901641
 
0.8%
45001
 
0.8%
400010
7.6%
37021
 
0.8%
36641
 
0.8%
36071
 
0.8%
32803
 
2.3%
21001
 
0.8%
1901.641
 
0.8%
19013
 
2.3%

Region
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct44
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
south of minas
23 
san marcos
11 
comayagua
10 
oriente
10 
tarrazu
Other values (39)
70 

Length

Max length46
Median length10
Mean length12.86363636
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)15.2%

Sample

1st rowleye, alishan township, chiayi county
2nd rowdongshan dist., tainan city 臺南市東山區
3rd rowapaneca
4th rowtarrazu
5th rowacatenango

Common Values

ValueCountFrequency (%)
south of minas23
17.4%
san marcos11
 
8.3%
comayagua10
 
7.6%
oriente10
 
7.6%
tarrazu8
 
6.1%
santa rosa7
 
5.3%
huehuetenango4
 
3.0%
apaneca4
 
3.0%
jalapa3
 
2.3%
antigua3
 
2.3%
Other values (34)49
37.1%

Length

2022-01-07T12:00:27.292293image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
minas24
 
8.9%
south23
 
8.5%
of23
 
8.5%
oriente12
 
4.4%
san11
 
4.1%
marcos11
 
4.1%
comayagua10
 
3.7%
tarrazu8
 
3.0%
dist7
 
2.6%
tainan7
 
2.6%
Other values (56)134
49.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Producer
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct62
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Ipanema Agricola
12 
Ipanema Agricola S.A
11 
JESUS RAMIREZ
 
7
ANGEL DE LEON
 
5
JORGE LEAL
 
5
Other values (57)
92 

Length

Max length61
Median length16
Mean length17.62878788
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)27.3%

Sample

1st rowFANG,ZHENG-LUN 方政倫
2nd rowWang Chao Yung 王超永
3rd rowJ.J. Borja Nathan
4th rowJohanna
5th rowJUAN BOCK

Common Values

ValueCountFrequency (%)
Ipanema Agricola12
 
9.1%
Ipanema Agricola S.A11
 
8.3%
JESUS RAMIREZ7
 
5.3%
ANGEL DE LEON5
 
3.8%
JORGE LEAL5
 
3.8%
Reinerio Zepeda5
 
3.8%
OTTO BECKER4
 
3.0%
Nahun Maldonado4
 
3.0%
CHAPULTEPEC4
 
3.0%
Martin Gutierrez4
 
3.0%
Other values (52)71
53.8%

Length

2022-01-07T12:00:27.466149image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
agricola28
 
7.7%
ipanema23
 
6.3%
s.a19
 
5.2%
de15
 
4.1%
ramirez8
 
2.2%
jesus7
 
1.9%
jorge7
 
1.9%
el6
 
1.6%
zepeda5
 
1.4%
maldonado5
 
1.4%
Other values (147)242
66.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Number.of.Bags
Real number (ℝ≥0)

HIGH CORRELATION

Distinct29
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.25
Minimum1
Maximum550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:27.634549image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q150
median250
Q3290
95-th percentile320
Maximum550
Range549
Interquartile range (IQR)240

Descriptive statistics

Standard deviation134.5387015
Coefficient of variation (CV)0.7071679451
Kurtosis-0.9841090492
Mean190.25
Median Absolute Deviation (MAD)70
Skewness0.04343515515
Sum25113
Variance18100.66221
MonotonicityNot monotonic
2022-01-07T12:00:27.769362image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
27529
22.0%
32025
18.9%
5015
11.4%
259
 
6.8%
207
 
5.3%
105
 
3.8%
2005
 
3.8%
1004
 
3.0%
2504
 
3.0%
153
 
2.3%
Other values (19)26
19.7%
ValueCountFrequency (%)
11
 
0.8%
31
 
0.8%
61
 
0.8%
82
 
1.5%
105
 
3.8%
153
 
2.3%
207
5.3%
259
6.8%
5015
11.4%
801
 
0.8%
ValueCountFrequency (%)
5502
 
1.5%
4501
 
0.8%
4401
 
0.8%
3771
 
0.8%
3251
 
0.8%
32025
18.9%
3101
 
0.8%
3051
 
0.8%
2851
 
0.8%
27529
22.0%

Bag.Weight
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
69 kg
77 
60 kg
29 
50 kg
 
5
20 kg
 
3
10 kg
 
3
Other values (11)
15 

Length

Max length8
Median length5
Mean length5.022727273
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)6.1%

Sample

1st row50 kg
2nd row20 kg
3rd row69 kg
4th row69 kg
5th row34 kg

Common Values

ValueCountFrequency (%)
69 kg77
58.3%
60 kg29
 
22.0%
50 kg5
 
3.8%
20 kg3
 
2.3%
10 kg3
 
2.3%
59 kg3
 
2.3%
30 kg2
 
1.5%
5 kg2
 
1.5%
34 kg1
 
0.8%
12000 kg1
 
0.8%
Other values (6)6
 
4.5%

Length

2022-01-07T12:00:27.923304image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kg132
50.0%
6977
29.2%
6029
 
11.0%
505
 
1.9%
203
 
1.1%
103
 
1.1%
593
 
1.1%
52
 
0.8%
302
 
0.8%
341
 
0.4%
Other values (7)7
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

In.Country.Partner
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Asociacion Nacional Del Café
45 
Brazil Specialty Coffee Association
26 
Instituto Hondureño del Café
22 
Specialty Coffee Association
17 
Specialty Coffee Association of Costa Rica
Other values (5)
14 

Length

Max length42
Median length28
Mean length30.23484848
Min length22

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)2.3%

Sample

1st rowSpecialty Coffee Association
2nd rowSpecialty Coffee Association
3rd rowSalvadoran Coffee Council
4th rowSpecialty Coffee Association of Costa Rica
5th rowAsociacion Nacional Del Café

Common Values

ValueCountFrequency (%)
Asociacion Nacional Del Café45
34.1%
Brazil Specialty Coffee Association26
19.7%
Instituto Hondureño del Café22
16.7%
Specialty Coffee Association17
 
12.9%
Specialty Coffee Association of Costa Rica8
 
6.1%
Salvadoran Coffee Council7
 
5.3%
Uganda Coffee Development Authority4
 
3.0%
Centro Agroecológico del Café A.C.1
 
0.8%
Tanzanian Coffee Board1
 
0.8%
Yunnan Coffee Exchange1
 
0.8%

Length

2022-01-07T12:00:28.088822image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-07T12:00:28.210281image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
del68
13.1%
café68
13.1%
coffee64
12.3%
specialty51
9.8%
association51
9.8%
asociacion45
8.7%
nacional45
8.7%
brazil26
 
5.0%
instituto22
 
4.2%
hondureño22
 
4.2%
Other values (15)57
11.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Harvest.Year
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2016
56 
2017
40 
2015
22 
2017 / 2018
12 
2016 / 2017
 
2

Length

Max length11
Median length4
Mean length4.742424242
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2016
3rd row2016
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
201656
42.4%
201740
30.3%
201522
 
16.7%
2017 / 201812
 
9.1%
2016 / 20172
 
1.5%

Length

2022-01-07T12:00:28.394020image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-07T12:00:28.492616image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
201658
36.2%
201754
33.8%
201522
 
13.8%
14
 
8.8%
201812
 
7.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Grading.Date
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct60
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
October 20th, 2017
11 
August 16th, 2016
 
9
June 1st, 2017
 
8
August 22nd, 2017
 
7
May 18th, 2016
 
6
Other values (55)
91 

Length

Max length20
Median length16
Mean length15.84090909
Min length13

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)25.8%

Sample

1st rowMay 18th, 2016
2nd rowMay 18th, 2016
3rd rowJune 26th, 2017
4th rowOctober 4th, 2016
5th rowJune 1st, 2016

Common Values

ValueCountFrequency (%)
October 20th, 201711
 
8.3%
August 16th, 20169
 
6.8%
June 1st, 20178
 
6.1%
August 22nd, 20177
 
5.3%
May 18th, 20166
 
4.5%
June 22nd, 20175
 
3.8%
August 23rd, 20175
 
3.8%
April 6th, 20174
 
3.0%
May 23rd, 20164
 
3.0%
September 8th, 20174
 
3.0%
Other values (50)69
52.3%

Length

2022-01-07T12:00:28.620195image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
201776
19.2%
201656
14.1%
august32
 
8.1%
june31
 
7.8%
may17
 
4.3%
october14
 
3.5%
20th14
 
3.5%
22nd13
 
3.3%
23rd13
 
3.3%
16th11
 
2.8%
Other values (32)119
30.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Owner.1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Juan Luis Alvarado Romero
45 
Ipanema Coffees
23 
Lin, Che-Hao Krude 林哲豪
13 
Bismarck Castro
11 
Consejo Salvadoreño del Café
Other values (19)
33 

Length

Max length41
Median length22
Mean length20.20454545
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)6.1%

Sample

1st rowLin, Che-Hao Krude 林哲豪
2nd rowLin, Che-Hao Krude 林哲豪
3rd rowConsejo Salvadoreño del Café
4th rowRodrigo Soto
5th rowJuan Luis Alvarado Romero

Common Values

ValueCountFrequency (%)
Juan Luis Alvarado Romero45
34.1%
Ipanema Coffees23
17.4%
Lin, Che-Hao Krude 林哲豪13
 
9.8%
Bismarck Castro11
 
8.3%
Consejo Salvadoreño del Café7
 
5.3%
CECA, S.A.4
 
3.0%
Exportadora Atlantic, S.A.3
 
2.3%
Janny Marlith Torres2
 
1.5%
Gabriel Barbara2
 
1.5%
Elsy Reyes2
 
1.5%
Other values (14)20
15.2%

Length

2022-01-07T12:00:28.916324image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
juan45
 
10.9%
alvarado45
 
10.9%
romero45
 
10.9%
luis45
 
10.9%
ipanema23
 
5.6%
coffees23
 
5.6%
lin13
 
3.2%
che-hao13
 
3.2%
krude13
 
3.2%
林哲豪13
 
3.2%
Other values (54)133
32.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Variety
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Bourbon
62 
Caturra
33 
Catuai
10 
Typica
Yellow Bourbon
 
5
Other values (7)
14 

Length

Max length14
Median length7
Mean length7.045454545
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)2.3%

Sample

1st rowSumatra
2nd rowTypica
3rd rowBourbon
4th rowCatuai
5th rowBourbon

Common Values

ValueCountFrequency (%)
Bourbon62
47.0%
Caturra33
25.0%
Catuai10
 
7.6%
Typica8
 
6.1%
Yellow Bourbon5
 
3.8%
Pacas4
 
3.0%
Other3
 
2.3%
SL142
 
1.5%
Mundo Novo2
 
1.5%
Sumatra1
 
0.8%
Other values (2)2
 
1.5%

Length

2022-01-07T12:00:29.056073image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bourbon67
48.2%
caturra33
23.7%
catuai10
 
7.2%
typica8
 
5.8%
yellow5
 
3.6%
pacas4
 
2.9%
other3
 
2.2%
sl142
 
1.4%
mundo2
 
1.4%
novo2
 
1.4%
Other values (3)3
 
2.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Processing.Method
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Washed / Wet
88 
Natural / Dry
34 
Pulped natural / honey
 
5
Other
 
5

Length

Max length22
Median length12
Mean length12.37121212
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPulped natural / honey
2nd rowNatural / Dry
3rd rowWashed / Wet
4th rowWashed / Wet
5th rowWashed / Wet

Common Values

ValueCountFrequency (%)
Washed / Wet88
66.7%
Natural / Dry34
 
25.8%
Pulped natural / honey5
 
3.8%
Other5
 
3.8%

Length

2022-01-07T12:00:29.203214image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-07T12:00:29.306641image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
127
32.5%
washed88
22.5%
wet88
22.5%
natural39
 
10.0%
dry34
 
8.7%
pulped5
 
1.3%
honey5
 
1.3%
other5
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Aroma
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.568409091
Minimum7.08
Maximum8.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:29.408862image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum7.08
5-th percentile7.294
Q17.5
median7.58
Q37.67
95-th percentile7.83
Maximum8.08
Range1
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.1681458361
Coefficient of variation (CV)0.02221680067
Kurtosis0.8308716819
Mean7.568409091
Median Absolute Deviation (MAD)0.09
Skewness0.0576267271
Sum999.03
Variance0.02827302221
MonotonicityNot monotonic
2022-01-07T12:00:29.538542image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
7.532
24.2%
7.5826
19.7%
7.6724
18.2%
7.4216
12.1%
7.7511
 
8.3%
7.835
 
3.8%
7.335
 
3.8%
7.924
 
3.0%
7.173
 
2.3%
7.253
 
2.3%
Other values (3)3
 
2.3%
ValueCountFrequency (%)
7.081
 
0.8%
7.173
 
2.3%
7.253
 
2.3%
7.335
 
3.8%
7.4216
12.1%
7.532
24.2%
7.5826
19.7%
7.6724
18.2%
7.7511
 
8.3%
7.835
 
3.8%
ValueCountFrequency (%)
8.081
 
0.8%
81
 
0.8%
7.924
 
3.0%
7.835
 
3.8%
7.7511
 
8.3%
7.6724
18.2%
7.5826
19.7%
7.532
24.2%
7.4216
12.1%
7.335
 
3.8%

Flavor
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.55530303
Minimum6.58
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:29.678530image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum6.58
5-th percentile7.08
Q17.5
median7.58
Q37.69
95-th percentile7.83
Maximum8
Range1.42
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.2179482374
Coefficient of variation (CV)0.02884705438
Kurtosis2.729616835
Mean7.55530303
Median Absolute Deviation (MAD)0.09
Skewness-1.112027239
Sum997.3
Variance0.04750143419
MonotonicityNot monotonic
2022-01-07T12:00:29.808129image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
7.530
22.7%
7.5819
14.4%
7.6719
14.4%
7.7518
13.6%
7.8311
 
8.3%
7.4211
 
8.3%
7.338
 
6.1%
7.084
 
3.0%
7.254
 
3.0%
7.923
 
2.3%
Other values (3)5
 
3.8%
ValueCountFrequency (%)
6.581
 
0.8%
73
 
2.3%
7.084
 
3.0%
7.254
 
3.0%
7.338
 
6.1%
7.4211
 
8.3%
7.530
22.7%
7.5819
14.4%
7.6719
14.4%
7.7518
13.6%
ValueCountFrequency (%)
81
 
0.8%
7.923
 
2.3%
7.8311
 
8.3%
7.7518
13.6%
7.6719
14.4%
7.5819
14.4%
7.530
22.7%
7.4211
 
8.3%
7.338
 
6.1%
7.254
 
3.0%

Aftertaste
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.404393939
Minimum6.33
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:29.945772image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum6.33
5-th percentile7
Q17.25
median7.42
Q37.58
95-th percentile7.75
Maximum8
Range1.67
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.242210739
Coefficient of variation (CV)0.03271175751
Kurtosis2.328305482
Mean7.404393939
Median Absolute Deviation (MAD)0.16
Skewness-0.8273271807
Sum977.38
Variance0.0586660421
MonotonicityNot monotonic
2022-01-07T12:00:30.078221image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
7.5820
15.2%
7.3319
14.4%
7.518
13.6%
7.4218
13.6%
7.2516
12.1%
7.6710
7.6%
7.176
 
4.5%
7.755
 
3.8%
7.085
 
3.8%
7.834
 
3.0%
Other values (5)11
8.3%
ValueCountFrequency (%)
6.331
 
0.8%
6.751
 
0.8%
6.924
 
3.0%
74
 
3.0%
7.085
 
3.8%
7.176
 
4.5%
7.2516
12.1%
7.3319
14.4%
7.4218
13.6%
7.518
13.6%
ValueCountFrequency (%)
81
 
0.8%
7.834
 
3.0%
7.755
 
3.8%
7.6710
7.6%
7.5820
15.2%
7.518
13.6%
7.4218
13.6%
7.3319
14.4%
7.2516
12.1%
7.176
 
4.5%

Acidity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.581136364
Minimum6.25
Maximum8.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:30.218480image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum6.25
5-th percentile7.1295
Q17.5
median7.58
Q37.75
95-th percentile7.92
Maximum8.25
Range2
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.2606967989
Coefficient of variation (CV)0.03438756229
Kurtosis5.025161266
Mean7.581136364
Median Absolute Deviation (MAD)0.16
Skewness-1.31189194
Sum1000.71
Variance0.06796282096
MonotonicityNot monotonic
2022-01-07T12:00:30.356128image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
7.6727
20.5%
7.520
15.2%
7.5818
13.6%
7.7514
10.6%
7.4213
9.8%
7.8310
 
7.6%
7.926
 
4.5%
7.336
 
4.5%
84
 
3.0%
7.174
 
3.0%
Other values (8)10
 
7.6%
ValueCountFrequency (%)
6.251
 
0.8%
6.831
 
0.8%
6.921
 
0.8%
72
 
1.5%
7.082
 
1.5%
7.174
 
3.0%
7.251
 
0.8%
7.336
 
4.5%
7.4213
9.8%
7.520
15.2%
ValueCountFrequency (%)
8.251
 
0.8%
8.081
 
0.8%
84
 
3.0%
7.926
 
4.5%
7.8310
 
7.6%
7.7514
10.6%
7.6727
20.5%
7.5818
13.6%
7.520
15.2%
7.4213
9.8%

Body
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.537272727
Minimum6.42
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:30.496444image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum6.42
5-th percentile7.25
Q17.42
median7.5
Q37.67
95-th percentile7.8705
Maximum8
Range1.58
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.2269414657
Coefficient of variation (CV)0.03010922836
Kurtosis3.864733037
Mean7.537272727
Median Absolute Deviation (MAD)0.17
Skewness-0.9416289089
Sum994.92
Variance0.05150242887
MonotonicityNot monotonic
2022-01-07T12:00:30.631739image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
7.522
16.7%
7.6721
15.9%
7.5820
15.2%
7.4220
15.2%
7.3314
10.6%
7.8312
9.1%
7.755
 
3.8%
7.255
 
3.8%
7.924
 
3.0%
74
 
3.0%
Other values (3)5
 
3.8%
ValueCountFrequency (%)
6.421
 
0.8%
74
 
3.0%
7.171
 
0.8%
7.255
 
3.8%
7.3314
10.6%
7.4220
15.2%
7.522
16.7%
7.5820
15.2%
7.6721
15.9%
7.755
 
3.8%
ValueCountFrequency (%)
83
 
2.3%
7.924
 
3.0%
7.8312
9.1%
7.755
 
3.8%
7.6721
15.9%
7.5820
15.2%
7.522
16.7%
7.4220
15.2%
7.3314
10.6%
7.255
 
3.8%

Balance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.505075758
Minimum6.08
Maximum8.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:30.783426image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum6.08
5-th percentile7.044
Q17.42
median7.5
Q37.67
95-th percentile7.83
Maximum8.17
Range2.09
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.2538632685
Coefficient of variation (CV)0.03382554377
Kurtosis7.096435901
Mean7.505075758
Median Absolute Deviation (MAD)0.125
Skewness-1.503936878
Sum990.67
Variance0.0644465591
MonotonicityNot monotonic
2022-01-07T12:00:30.899516image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7.530
22.7%
7.5821
15.9%
7.6717
12.9%
7.4215
11.4%
7.839
 
6.8%
7.758
 
6.1%
7.338
 
6.1%
7.258
 
6.1%
7.174
 
3.0%
73
 
2.3%
Other values (6)9
 
6.8%
ValueCountFrequency (%)
6.081
 
0.8%
6.923
 
2.3%
73
 
2.3%
7.081
 
0.8%
7.174
 
3.0%
7.258
 
6.1%
7.338
 
6.1%
7.4215
11.4%
7.530
22.7%
7.5821
15.9%
ValueCountFrequency (%)
8.171
 
0.8%
82
 
1.5%
7.921
 
0.8%
7.839
 
6.8%
7.758
 
6.1%
7.6717
12.9%
7.5821
15.9%
7.530
22.7%
7.4215
11.4%
7.338
 
6.1%

Uniformity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.863484848
Minimum6
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:31.020149image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile9.33
Q110
median10
Q310
95-th percentile10
Maximum10
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5257259392
Coefficient of variation (CV)0.05330022272
Kurtosis32.8847162
Mean9.863484848
Median Absolute Deviation (MAD)0
Skewness-5.396970922
Sum1301.98
Variance0.2763877631
MonotonicityNot monotonic
2022-01-07T12:00:31.135860image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
10118
89.4%
9.339
 
6.8%
8.672
 
1.5%
6.671
 
0.8%
81
 
0.8%
61
 
0.8%
ValueCountFrequency (%)
61
 
0.8%
6.671
 
0.8%
81
 
0.8%
8.672
 
1.5%
9.339
 
6.8%
10118
89.4%
ValueCountFrequency (%)
10118
89.4%
9.339
 
6.8%
8.672
 
1.5%
81
 
0.8%
6.671
 
0.8%
61
 
0.8%

Clean.Cup
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
10.0
125 
9.33
 
4
8.67
 
1
8.0
 
1
6.0
 
1

Length

Max length4
Median length4
Mean length3.984848485
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)2.3%

Sample

1st row10.0
2nd row10.0
3rd row10.0
4th row10.0
5th row10.0

Common Values

ValueCountFrequency (%)
10.0125
94.7%
9.334
 
3.0%
8.671
 
0.8%
8.01
 
0.8%
6.01
 
0.8%

Length

2022-01-07T12:00:31.281265image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-07T12:00:31.372470image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
10.0125
94.7%
9.334
 
3.0%
8.671
 
0.8%
8.01
 
0.8%
6.01
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sweetness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.896363636
Minimum6
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:31.466952image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile9.33
Q110
median10
Q310
95-th percentile10
Maximum10
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4658854702
Coefficient of variation (CV)0.04707643002
Kurtosis42.70593986
Mean9.896363636
Median Absolute Deviation (MAD)0
Skewness-6.098378696
Sum1306.32
Variance0.2170492713
MonotonicityNot monotonic
2022-01-07T12:00:31.583684image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
10122
92.4%
9.336
 
4.5%
8.671
 
0.8%
61
 
0.8%
7.921
 
0.8%
7.751
 
0.8%
ValueCountFrequency (%)
61
 
0.8%
7.751
 
0.8%
7.921
 
0.8%
8.671
 
0.8%
9.336
 
4.5%
10122
92.4%
ValueCountFrequency (%)
10122
92.4%
9.336
 
4.5%
8.671
 
0.8%
7.921
 
0.8%
7.751
 
0.8%
61
 
0.8%

Cupper.Points
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct20
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.498484848
Minimum5.17
Maximum8.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:31.725601image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum5.17
5-th percentile7
Q17.33
median7.5
Q37.69
95-th percentile7.92
Maximum8.5
Range3.33
Interquartile range (IQR)0.36

Descriptive statistics

Standard deviation0.4054071271
Coefficient of variation (CV)0.05406520588
Kurtosis14.9149913
Mean7.498484848
Median Absolute Deviation (MAD)0.17
Skewness-2.927023597
Sum989.8
Variance0.1643549387
MonotonicityNot monotonic
2022-01-07T12:00:31.857319image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
7.527
20.5%
7.5816
12.1%
7.6713
9.8%
7.3312
9.1%
7.8311
8.3%
7.7511
8.3%
7.2510
 
7.6%
7.429
 
6.8%
75
 
3.8%
7.925
 
3.8%
Other values (10)13
9.8%
ValueCountFrequency (%)
5.171
 
0.8%
5.251
 
0.8%
6.171
 
0.8%
6.751
 
0.8%
6.831
 
0.8%
75
3.8%
7.081
 
0.8%
7.171
 
0.8%
7.2510
7.6%
7.3312
9.1%
ValueCountFrequency (%)
8.51
 
0.8%
8.171
 
0.8%
84
 
3.0%
7.925
 
3.8%
7.8311
8.3%
7.7511
8.3%
7.6713
9.8%
7.5816
12.1%
7.527
20.5%
7.429
 
6.8%

Total.Cup.Points
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.33515152
Minimum63.08
Maximum86.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:32.150869image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum63.08
5-th percentile79.17
Q181.73
median82.75
Q383.58
95-th percentile84.42
Maximum86.58
Range23.5
Interquartile range (IQR)1.85

Descriptive statistics

Standard deviation2.263464036
Coefficient of variation (CV)0.02749085894
Kurtosis39.5598785
Mean82.33515152
Median Absolute Deviation (MAD)0.92
Skewness-4.921454797
Sum10868.24
Variance5.123269443
MonotonicityNot monotonic
2022-01-07T12:00:32.326627image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.7510
 
7.6%
83.176
 
4.5%
835
 
3.8%
84.175
 
3.8%
83.675
 
3.8%
83.924
 
3.0%
81.674
 
3.0%
81.834
 
3.0%
81.924
 
3.0%
82.54
 
3.0%
Other values (46)81
61.4%
ValueCountFrequency (%)
63.081
 
0.8%
77.331
 
0.8%
781
 
0.8%
78.831
 
0.8%
79.081
 
0.8%
79.173
2.3%
79.672
1.5%
79.751
 
0.8%
79.831
 
0.8%
801
 
0.8%
ValueCountFrequency (%)
86.581
 
0.8%
84.831
 
0.8%
84.673
2.3%
84.51
 
0.8%
84.422
 
1.5%
84.331
 
0.8%
84.253
2.3%
84.175
3.8%
84.081
 
0.8%
841
 
0.8%

Moisture
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09037878788
Minimum0
Maximum0.13
Zeros20
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:32.459989image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median0.1
Q30.11
95-th percentile0.12
Maximum0.13
Range0.13
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.03908115071
Coefficient of variation (CV)0.4324150791
Kurtosis1.576563491
Mean0.09037878788
Median Absolute Deviation (MAD)0.01
Skewness-1.806844865
Sum11.93
Variance0.001527336341
MonotonicityNot monotonic
2022-01-07T12:00:32.591894image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.1150
37.9%
0.141
31.1%
020
 
15.2%
0.1212
 
9.1%
0.097
 
5.3%
0.132
 
1.5%
ValueCountFrequency (%)
020
 
15.2%
0.097
 
5.3%
0.141
31.1%
0.1150
37.9%
0.1212
 
9.1%
0.132
 
1.5%
ValueCountFrequency (%)
0.132
 
1.5%
0.1212
 
9.1%
0.1150
37.9%
0.141
31.1%
0.097
 
5.3%
020
 
15.2%

Category.One.Defects
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3560606061
Minimum0
Maximum31
Zeros124
Zeros (%)93.9%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:32.709089image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum31
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.763261089
Coefficient of variation (CV)7.760648164
Kurtosis117.946336
Mean0.3560606061
Median Absolute Deviation (MAD)0
Skewness10.64561031
Sum47
Variance7.635611844
MonotonicityNot monotonic
2022-01-07T12:00:32.821151image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0124
93.9%
13
 
2.3%
22
 
1.5%
311
 
0.8%
61
 
0.8%
31
 
0.8%
ValueCountFrequency (%)
0124
93.9%
13
 
2.3%
22
 
1.5%
31
 
0.8%
61
 
0.8%
311
 
0.8%
ValueCountFrequency (%)
311
 
0.8%
61
 
0.8%
31
 
0.8%
22
 
1.5%
13
 
2.3%
0124
93.9%

Quakers
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7196969697
Minimum0
Maximum8
Zeros86
Zeros (%)65.2%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:32.950301image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2.45
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.338304692
Coefficient of variation (CV)1.859539151
Kurtosis10.31215613
Mean0.7196969697
Median Absolute Deviation (MAD)0
Skewness2.85599572
Sum95
Variance1.791059449
MonotonicityNot monotonic
2022-01-07T12:00:33.063055image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
086
65.2%
220
 
15.2%
119
 
14.4%
42
 
1.5%
52
 
1.5%
71
 
0.8%
31
 
0.8%
81
 
0.8%
ValueCountFrequency (%)
086
65.2%
119
 
14.4%
220
 
15.2%
31
 
0.8%
42
 
1.5%
52
 
1.5%
71
 
0.8%
81
 
0.8%
ValueCountFrequency (%)
81
 
0.8%
71
 
0.8%
52
 
1.5%
42
 
1.5%
31
 
0.8%
220
 
15.2%
119
 
14.4%
086
65.2%

Color
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Green
107 
Bluish-Green
15 
Blue-Green
 
10

Length

Max length12
Median length5
Mean length6.174242424
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGreen
2nd rowGreen
3rd rowBlue-Green
4th rowBlue-Green
5th rowGreen

Common Values

ValueCountFrequency (%)
Green107
81.1%
Bluish-Green15
 
11.4%
Blue-Green10
 
7.6%

Length

2022-01-07T12:00:33.202797image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-07T12:00:33.293878image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
green107
81.1%
bluish-green15
 
11.4%
blue-green10
 
7.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Category.Two.Defects
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.795454545
Minimum0
Maximum12
Zeros26
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:33.384043image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile7
Maximum12
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.407807391
Coefficient of variation (CV)0.8613294733
Kurtosis0.9619631762
Mean2.795454545
Median Absolute Deviation (MAD)2
Skewness0.9473642596
Sum369
Variance5.797536433
MonotonicityNot monotonic
2022-01-07T12:00:33.519089image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
026
19.7%
122
16.7%
320
15.2%
219
14.4%
415
11.4%
513
9.8%
67
 
5.3%
74
 
3.0%
84
 
3.0%
101
 
0.8%
ValueCountFrequency (%)
026
19.7%
122
16.7%
219
14.4%
320
15.2%
415
11.4%
513
9.8%
67
 
5.3%
74
 
3.0%
84
 
3.0%
101
 
0.8%
ValueCountFrequency (%)
121
 
0.8%
101
 
0.8%
84
 
3.0%
74
 
3.0%
67
 
5.3%
513
9.8%
415
11.4%
320
15.2%
219
14.4%
122
16.7%

Expiration
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct60
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
October 20th, 2018
11 
August 16th, 2017
 
9
June 1st, 2018
 
8
August 22nd, 2018
 
7
May 18th, 2017
 
6
Other values (55)
91 

Length

Max length20
Median length16
Mean length15.84090909
Min length13

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)25.8%

Sample

1st rowMay 18th, 2017
2nd rowMay 18th, 2017
3rd rowJune 26th, 2018
4th rowOctober 4th, 2017
5th rowJune 1st, 2017

Common Values

ValueCountFrequency (%)
October 20th, 201811
 
8.3%
August 16th, 20179
 
6.8%
June 1st, 20188
 
6.1%
August 22nd, 20187
 
5.3%
May 18th, 20176
 
4.5%
June 22nd, 20185
 
3.8%
August 23rd, 20185
 
3.8%
April 6th, 20184
 
3.0%
May 23rd, 20174
 
3.0%
September 8th, 20184
 
3.0%
Other values (50)69
52.3%

Length

2022-01-07T12:00:33.662602image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
201876
19.2%
201756
14.1%
august32
 
8.1%
june31
 
7.8%
may17
 
4.3%
october14
 
3.5%
20th14
 
3.5%
22nd13
 
3.3%
23rd13
 
3.3%
16th11
 
2.8%
Other values (32)119
30.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

unit_of_measurement
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
m
115 
ft
17 

Length

Max length2
Median length1
Mean length1.128787879
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowm
2nd rowm
3rd rowm
4th rowm
5th rowm

Common Values

ValueCountFrequency (%)
m115
87.1%
ft17
 
12.9%

Length

2022-01-07T12:00:33.801153image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-07T12:00:33.893558image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
m115
87.1%
ft17
 
12.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

altitude_low_meters
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4153.672952
Minimum157.8864
Maximum190164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:34.001151image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum157.8864
5-th percentile650
Q1941.5
median1250
Q31573.75
95-th percentile1901
Maximum190164
Range190006.1136
Interquartile range (IQR)632.25

Descriptive statistics

Standard deviation23164.90414
Coefficient of variation (CV)5.576968724
Kurtosis63.37764574
Mean4153.672952
Median Absolute Deviation (MAD)315.5
Skewness8.02412018
Sum548284.8296
Variance536612783.8
MonotonicityNot monotonic
2022-01-07T12:00:34.179506image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
170011
 
8.3%
89011
 
8.3%
1219.210
 
7.6%
15009
 
6.8%
14007
 
5.3%
9345
 
3.8%
12505
 
3.8%
14504
 
3.0%
18504
 
3.0%
12004
 
3.0%
Other values (39)62
47.0%
ValueCountFrequency (%)
157.88643
 
2.3%
1681
 
0.8%
3502
 
1.5%
6502
 
1.5%
6801
 
0.8%
7501
 
0.8%
8001
 
0.8%
8721
 
0.8%
89011
8.3%
8942
 
1.5%
ValueCountFrequency (%)
1901642
 
1.5%
32803
 
2.3%
21001
 
0.8%
19013
 
2.3%
18504
 
3.0%
18002
 
1.5%
17501
 
0.8%
170011
8.3%
16502
 
1.5%
16451
 
0.8%

altitude_high_meters
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4153.672952
Minimum157.8864
Maximum190164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:34.370943image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum157.8864
5-th percentile650
Q1941.5
median1250
Q31573.75
95-th percentile1901
Maximum190164
Range190006.1136
Interquartile range (IQR)632.25

Descriptive statistics

Standard deviation23164.90414
Coefficient of variation (CV)5.576968724
Kurtosis63.37764574
Mean4153.672952
Median Absolute Deviation (MAD)315.5
Skewness8.02412018
Sum548284.8296
Variance536612783.8
MonotonicityNot monotonic
2022-01-07T12:00:34.549522image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
170011
 
8.3%
89011
 
8.3%
1219.210
 
7.6%
15009
 
6.8%
14007
 
5.3%
9345
 
3.8%
12505
 
3.8%
14504
 
3.0%
18504
 
3.0%
12004
 
3.0%
Other values (39)62
47.0%
ValueCountFrequency (%)
157.88643
 
2.3%
1681
 
0.8%
3502
 
1.5%
6502
 
1.5%
6801
 
0.8%
7501
 
0.8%
8001
 
0.8%
8721
 
0.8%
89011
8.3%
8942
 
1.5%
ValueCountFrequency (%)
1901642
 
1.5%
32803
 
2.3%
21001
 
0.8%
19013
 
2.3%
18504
 
3.0%
18002
 
1.5%
17501
 
0.8%
170011
8.3%
16502
 
1.5%
16451
 
0.8%

altitude_mean_meters
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4153.672952
Minimum157.8864
Maximum190164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2022-01-07T12:00:34.727695image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum157.8864
5-th percentile650
Q1941.5
median1250
Q31573.75
95-th percentile1901
Maximum190164
Range190006.1136
Interquartile range (IQR)632.25

Descriptive statistics

Standard deviation23164.90414
Coefficient of variation (CV)5.576968724
Kurtosis63.37764574
Mean4153.672952
Median Absolute Deviation (MAD)315.5
Skewness8.02412018
Sum548284.8296
Variance536612783.8
MonotonicityNot monotonic
2022-01-07T12:00:34.912441image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
170011
 
8.3%
89011
 
8.3%
1219.210
 
7.6%
15009
 
6.8%
14007
 
5.3%
9345
 
3.8%
12505
 
3.8%
14504
 
3.0%
18504
 
3.0%
12004
 
3.0%
Other values (39)62
47.0%
ValueCountFrequency (%)
157.88643
 
2.3%
1681
 
0.8%
3502
 
1.5%
6502
 
1.5%
6801
 
0.8%
7501
 
0.8%
8001
 
0.8%
8721
 
0.8%
89011
8.3%
8942
 
1.5%
ValueCountFrequency (%)
1901642
 
1.5%
32803
 
2.3%
21001
 
0.8%
19013
 
2.3%
18504
 
3.0%
18002
 
1.5%
17501
 
0.8%
170011
8.3%
16502
 
1.5%
16451
 
0.8%

Interactions

2022-01-07T12:00:21.343595image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:31.543388image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:34.116974image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:36.940454image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:39.484378image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:42.264079image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:45.053107image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:47.744281image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:50.478325image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:53.015711image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:55.802804image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:58.483332image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:01.191771image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:03.996586image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:06.822224image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:09.478027image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:12.363051image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:15.305364image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:18.322100image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:21.493849image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:31.681150image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:34.565107image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:37.066069image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:39.627197image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:42.408260image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:45.208585image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:47.885615image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:50.608681image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:53.160550image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:55.950318image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:58.626619image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:01.376845image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:04.143880image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:06.959423image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:09.782297image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:12.507538image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:15.456730image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:18.477761image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:21.635471image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:31.808478image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:34.694025image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:37.187326image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:39.752867image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:42.536189image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:45.344005image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:48.007638image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:50.725691image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:53.291683image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:56.089669image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:58.893004image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:01.558423image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:04.294110image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:07.088583image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:09.921622image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:12.639317image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:15.598841image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:18.618077image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:21.775101image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:31.937733image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:34.805141image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:37.304381image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:39.882967image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:42.659168image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:45.467824image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:48.132935image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:50.837342image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:53.411562image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:56.226189image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:59.020149image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:01.715409image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:04.422932image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:07.218355image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:10.048451image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:12.765568image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:15.776774image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:18.751444image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:21.923460image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:32.075415image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:34.934479image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:37.429863image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:40.012762image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:42.799881image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:45.609918image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:48.269522image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:50.965924image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:53.560996image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:56.366241image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:59.149364image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:01.852957image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:04.555879image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:07.364458image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:10.198036image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:12.918126image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:15.941899image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:18.891186image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:22.075517image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:32.213728image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:35.068996image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:37.561128image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:40.141814image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:42.944615image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:45.781654image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:48.550425image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:51.095833image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:53.706636image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:56.516148image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:59.281601image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:01.984867image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:04.702379image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:07.510381image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:10.344045image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:13.054209image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:16.095595image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:19.044989image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T12:00:22.234627image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:32.361402image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-01-07T11:59:33.839815image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-07T11:59:36.646025image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-01-07T12:00:01.046466image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-01-07T12:00:21.195211image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2022-01-07T12:00:35.242929image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-07T12:00:35.539668image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-07T12:00:35.853016image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-07T12:00:36.141150image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-07T12:00:36.436938image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-07T12:00:24.393421image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-07T12:00:25.725481image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

SpeciesOwnerCountry.of.OriginFarm.NameCompanyAltitudeRegionProducerNumber.of.BagsBag.WeightIn.Country.PartnerHarvest.YearGrading.DateOwner.1VarietyProcessing.MethodAromaFlavorAftertasteAcidityBodyBalanceUniformityClean.CupSweetnessCupper.PointsTotal.Cup.PointsMoistureCategory.One.DefectsQuakersColorCategory.Two.DefectsExpirationunit_of_measurementaltitude_low_metersaltitude_high_metersaltitude_mean_meters
0Arabicalin, che-hao krude 林哲豪Taiwantsoustructive garden 鄒築園taiwan coffee laboratory1200.0leye, alishan township, chiayi countyFANG,ZHENG-LUN 方政倫2050 kgSpecialty Coffee Association2015May 18th, 2016Lin, Che-Hao Krude 林哲豪SumatraPulped natural / honey8.008.008.008.258.008.1710.010.010.08.1786.580.0000.0Green0May 18th, 2017m1200.01200.01200.0
1Arabicalin, che-hao krude 林哲豪Taiwanshi fang yuan 十方源taiwan coffee laboratory350.0dongshan dist., tainan city 臺南市東山區Wang Chao Yung 王超永1020 kgSpecialty Coffee Association2016May 18th, 2016Lin, Che-Hao Krude 林哲豪TypicaNatural / Dry7.927.587.837.837.837.8310.010.010.08.0084.830.0000.0Green0May 18th, 2017m350.0350.0350.0
2Arabicaconsejo salvadoreño del caféEl Salvadormonterreycafetalera del pacifico1350.0apanecaJ.J. Borja Nathan15069 kgSalvadoran Coffee Council2016June 26th, 2017Consejo Salvadoreño del CaféBourbonWashed / Wet7.837.837.508.007.837.6710.010.010.08.0084.670.0000.0Blue-Green2June 26th, 2018m1350.01350.01350.0
3Arabicarodrigo sotoCosta Ricario jorcopanamerican coffee trading1150.0tarrazuJohanna1569 kgSpecialty Coffee Association of Costa Rica2015October 4th, 2016Rodrigo SotoCatuaiWashed / Wet8.087.757.677.837.507.9210.010.010.07.9284.670.1000.0Blue-Green2October 4th, 2017m1150.01150.01150.0
4Arabicajuan luis alvarado romeroGuatemalasan diego buena vistawaelti schoenfeld exportadores de cafe, s.a.1600.0acatenangoJUAN BOCK12034 kgAsociacion Nacional Del Café2015June 1st, 2016Juan Luis Alvarado RomeroBourbonWashed / Wet7.757.837.588.007.927.7510.010.010.07.8384.670.1000.0Green1June 1st, 2017m1600.01600.01600.0
5Arabicaecom japan limitedUgandakawacom sipi projectkawacom uganda ltd1750.0eastern ugandaKawacom Uganda Ltd20012000 kgUganda Coffee Development Authority2016March 14th, 2017ECOM Japan LimitedSL14Washed / Wet7.927.757.677.757.837.7510.010.010.07.8384.500.1100.0Green1March 14th, 2018m1750.01750.01750.0
6Arabicamax gurdianCosta Ricaseveral farmersbeneficios volcafé costa rica1300.0tarrazuBeneficio San Diego27569 kgSpecialty Coffee Association of Costa Rica2016March 23rd, 2017Max GurdianCaturraWashed / Wet7.587.837.838.007.677.5810.010.010.07.9284.420.1000.0Blue-Green3March 23rd, 2018m1300.01300.01300.0
7Arabicaaulia arif syahriIndonesiadarmawipt. olam indonesia1400.0sumatra brastagiSurbakti20030 kgSpecialty Coffee Association2017March 14th, 2017Aulia Arif SyahriMandhelingOther7.677.677.837.587.837.8310.010.010.08.0084.420.0000.0Blue-Green3March 14th, 2018m1400.01400.01400.0
8Arabicabismarck castroHonduraslos hicaquescigrah s.a de c.v1500.0central regionReinerio Zepeda27569 kgInstituto Hondureño del Café2016April 4th, 2016Bismarck CastroCatuaiWashed / Wet7.587.927.587.757.927.7510.010.010.07.8384.330.1101.0Green2April 4th, 2017m1500.01500.01500.0
9Arabicalin, che-hao krude 林哲豪Taiwan馨晴咖啡 good mood coffeetaiwan coffee laboratory1000.0國姓鄉 guoshing township黃美桃 Huang Mei Tao105 kgSpecialty Coffee Association2016August 10th, 2017Lin, Che-Hao Krude 林哲豪TypicaWashed / Wet7.927.757.757.677.677.8310.010.010.07.6784.250.1200.0Blue-Green0August 10th, 2018m1000.01000.01000.0

Last rows

SpeciesOwnerCountry.of.OriginFarm.NameCompanyAltitudeRegionProducerNumber.of.BagsBag.WeightIn.Country.PartnerHarvest.YearGrading.DateOwner.1VarietyProcessing.MethodAromaFlavorAftertasteAcidityBodyBalanceUniformityClean.CupSweetnessCupper.PointsTotal.Cup.PointsMoistureCategory.One.DefectsQuakersColorCategory.Two.DefectsExpirationunit_of_measurementaltitude_low_metersaltitude_high_metersaltitude_mean_meters
122Arabicajuan luis alvarado romeroGuatemalalas deliciasunex guatemala, s.a.4000.0santa rosaOTTO BECKER5069 kgAsociacion Nacional Del Café2017August 22nd, 2017Juan Luis Alvarado RomeroBourbonWashed / Wet7.337.427.337.427.507.428.679.339.337.4279.170.1000.0Green0August 22nd, 2018ft1219.21219.21219.2
123Arabicajanny marlith torresHondurasla bendicioncoffee planet corporation s.a1650.0ocotepequeJorge Alfredo Pinto1569 kgInstituto Hondureño del Café2017July 27th, 2017Janny Marlith TorresCatuaiWashed / Wet7.587.006.756.927.006.9210.0010.0010.007.0079.170.1100.0Green5July 27th, 2018m1650.01650.01650.0
124Arabicajuan luis alvarado romeroGuatemalachapultepecunex guatemala, s.a.1320.0occidenteCHAPULTEPEC25069 kgAsociacion Nacional Del Café2016May 23rd, 2016Juan Luis Alvarado RomeroBourbonWashed / Wet7.087.006.927.427.006.9210.0010.0010.006.8379.170.1108.0Green6May 23rd, 2017m1320.01320.01320.0
125Arabicajuan luis alvarado romeroGuatemalalas merceditasunex guatemala, s.a.1700.0san marcosANGEL DE LEON5069 kgAsociacion Nacional Del Café2017August 23rd, 2017Juan Luis Alvarado RomeroBourbonWashed / Wet7.587.677.427.427.677.678.678.678.677.6779.080.1000.0Green1August 23rd, 2018m1700.01700.01700.0
126Arabicaexportadora atlantic, s.a.Nicaraguafinca las maríasexportadora atlantic s.a1100.0jalapaTeófilo Narváez11 kgInstituto Hondureño del Café2016May 22nd, 2017Exportadora Atlantic, S.A.CaturraOther7.177.006.927.007.007.0010.0010.0010.006.7578.830.1002.0Green4May 22nd, 2018m1100.01100.01100.0
127Arabicalin, che-hao krude 林哲豪Taiwan大鋤花間 (hoe vs. flower coffee farm)taiwan coffee laboratory650.0台南市東山區( dongshan dist., tainan city)林俊吉( Lin, Chun-Chi)810 kgSpecialty Coffee Association2016June 6th, 2017Lin, Che-Hao Krude 林哲豪Yellow BourbonWashed / Wet7.507.087.337.177.507.506.6710.0010.007.2578.000.0030.0Green4June 6th, 2018m650.0650.0650.0
128Arabicaipanema coffeesBrazilfazenda capoeirnhaipanema coffees890.0south of minasIpanema Agricola S.A32060 kgBrazil Specialty Coffee Association2017 / 2018October 20th, 2017Ipanema CoffeesBourbonNatural / Dry7.507.337.427.587.586.928.008.0010.007.0077.330.0000.0Green3October 20th, 2018m890.0890.0890.0
129Arabicaexportadora atlantic, s.a.Nicaraguafinca las maríasexportadora atlantic s.a1100.0jalapaTeófilo Narváez55069 kgInstituto Hondureño del Café2016June 6th, 2017Exportadora Atlantic, S.A.CaturraOther7.256.586.336.256.426.086.006.006.006.1763.080.1310.0Green5June 6th, 2018m1100.01100.01100.0
130Robustanishant gurjerIndiasethuraman estatekaapi royale1000.0chikmagalur karnatakaSethuraman Estate Kaapi Royale32060 kgSpecialty Coffee Association2015August 17th, 2016Nishant GurjerOtherWashed / Wet7.677.757.587.837.838.0010.0010.007.927.9282.500.0900.0Green0August 17th, 2017m1000.01000.01000.0
131Robustanishant gurjerIndiasethuraman estate kaapi royalekaapi royale1000.0chikmagalur karnatakaSethuraman Estate Kaapi Royale32060 kgSpecialty Coffee Association2015August 23rd, 2016Nishant GurjerOtherNatural / Dry7.677.837.757.507.757.5810.0010.007.757.7581.580.1100.0Green0August 23rd, 2017m1000.01000.01000.0